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An Approximate Forecasting of Electricity Load and Price of a Smart Home Using Nearest Neighbor

  • Muhammad Nawaz
  • Nadeem JavaidEmail author
  • Fakhar Ullah Mangla
  • Maria Munir
  • Farwa Ihsan
  • Atia Javaid
  • Muhammad Asif
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 993)

Abstract

In Smart Grid, electricity demand and price forecasting literature has focused on Industrial, Buildings, and Residential sector demand, but this paper focuses on short term electricity demand and price forecasting for residential customer. Here we take smart meter data of hourly based from a smart home. First standardize and selected important features by using Recursive Feature Elimination with Linear Support Vector Classifier (RFE-LSVC). Second, do forecasting through K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Support Vector Regression (SVR) models and perform comparative analysis among models against four scenarios and provided best solution among all for individual scenario. This work proposed best solution of smart home’s load and price forecasting for smart grid to manage demand response efficiently. We evaluated every Models with Mean Absolute Percentage Error (MAPE).

References

  1. 1.
    Abedinia, O., Amjady, N., Zareipour, H.: A new feature selection technique for load and price forecast of electrical power systems. IEEE Trans. Power Syst. 32(1), 62–74 (2017)CrossRefGoogle Scholar
  2. 2.
    Aydarous, A., Elshahed, M.A., Hassan, M.M.: Short term load forecasting as a base core of smart grid integrated intelligent energy management system. In: 2017 International Conference on Modern Electrical and Energy Systems (MEES), pp. 192–195. IEEE, November 2017Google Scholar
  3. 3.
    Din, G.M.U., Mauthe, A.U., Marnerides, A.K.: Appliance-level short-term load forecasting using deep neural networks. In: 2018 International Conference on Computing, Networking and Communications (ICNC), pp. 53–57. IEEE, March 2018Google Scholar
  4. 4.
    Dong, X., Qian, L., Huang, L.: Short-term load forecasting in smart grid: a combined CNN and K-means clustering approach. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 119–125. IEEE, February 2017Google Scholar
  5. 5.
    Gao, J., Asamoah, K.O., Sifah, E.B., Smahi, A., Xia, Q., Xia, H., Zhang, X., Dong, G.: Gridmonitoring: secured sovereign blockchain based monitoring on smart grid. IEEE Access 6, 9917–9925 (2018)CrossRefGoogle Scholar
  6. 6.
    Jindal, A., Singh, M., Kumar, N.: Consumption-aware data analytical demand response scheme for peak load reduction in smart grid. IEEE Trans. Ind. Electron. 65(11), 8993–9004 (2018)CrossRefGoogle Scholar
  7. 7.
    Lago, J., De Ridder, F., Vrancx, P., De Schutter, B.: Forecasting day-ahead electricity prices in Europe: the importance of considering market integration. Appl. Energy 211, 890–903 (2018)CrossRefGoogle Scholar
  8. 8.
    Liu, Y., Wang, W., Ghadimi, N.: Electricity load forecasting by an improved forecast engine for building level consumers. Energy 139, 18–30 (2017)CrossRefGoogle Scholar
  9. 9.
    Luo, F., Dong, Z.Y., Liang, G., Murata, J., Xu, Z.: A distributed electricity trading system in active distribution networks based on multi-agent coalition and blockchain. IEEE Trans. Power Syst. (2018)Google Scholar
  10. 10.
    Lusis, P., Khalilpour, K.R., Andrew, L., Liebman, A.: Short-term residential load forecasting: impact of calendar effects and forecast granularity. Appl. Energy 205, 654–669 (2017)CrossRefGoogle Scholar
  11. 11.
    Moon, J., Kim, K.H., Kim, Y., Hwang, E.: A short-term electric load forecasting scheme using 2-stage predictive analytics. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 219–226. IEEE, January 2018Google Scholar
  12. 12.
    Raviv, E., Bouwman, K.E., Van Dijk, D.: Forecasting day-ahead electricity prices: utilizing hourly prices. Energy Econ. 50, 227–239 (2015)CrossRefGoogle Scholar
  13. 13.
    Sulaiman, S.M., Jeyanthy, P.A., Devaraj, D.: Artificial neural network based day ahead load forecasting using smart meter data. In: 2016 Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), pp. 1–6. IEEE, January 2016Google Scholar
  14. 14.
    Tudor, V., Almgren, M., Papatriantafilou, M.: Employing private data in AMI applications: short term load forecasting using differentially private aggregated data. In: 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 404–413. IEEE, July 2016Google Scholar
  15. 15.
    Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.: Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans. Big Data 5, 34–45 (2017)CrossRefGoogle Scholar
  16. 16.
    Wang, L., Zhang, Z., Chen, J.: Short-term electricity price forecasting with stacked denoising autoencoders. IEEE Trans. Power Syst. 32(4), 2673–2681 (2017)CrossRefGoogle Scholar
  17. 17.
    Zheng, H., Yuan, J., Chen, L.: Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation. Energies 10(8), 1168 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Muhammad Nawaz
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Fakhar Ullah Mangla
    • 2
  • Maria Munir
    • 2
  • Farwa Ihsan
    • 2
  • Atia Javaid
    • 1
  • Muhammad Asif
    • 3
  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.University of SargodhaSargodhaPakistan
  3. 3.The Islamia University of BahawalpurBahawalpurPakistan

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